Articles | Volume 21, issue 6
https://doi.org/10.5194/hess-21-2637-2017
https://doi.org/10.5194/hess-21-2637-2017
Research article
 | 
02 Jun 2017
Research article |  | 02 Jun 2017

Role of forcing uncertainty and background model error characterization in snow data assimilation

Sujay V. Kumar, Jiarui Dong, Christa D. Peters-Lidard, David Mocko, and Breogán Gómez

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by Editor) (06 Apr 2017) by Matthias Bernhardt
AR by Sujay Kumar on behalf of the Authors (07 Apr 2017)  Author's response   Manuscript 
ED: Publish as is (22 Apr 2017) by Matthias Bernhardt
AR by Sujay Kumar on behalf of the Authors (03 May 2017)
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Short summary
Data assimilation deals with the blending of model forecasts and observations based on their relative errors. This paper addresses the importance of accurately representing the errors in the model forecasts for skillful data assimilation performance.